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Feature Subset Selection for Malware Detection in Smart IoT Platforms

Abawajy, Jemal, Darem, A and Alhashmi, AA 2021, Feature Subset Selection for Malware Detection in Smart IoT Platforms, Sensors, vol. 21, no. 4, pp. 1-19, doi: 10.3390/s21041374.

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Title Feature Subset Selection for Malware Detection in Smart IoT Platforms
Author(s) Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Darem, A
Alhashmi, AA
Journal name Sensors
Volume number 21
Issue number 4
Article ID 1374
Start page 1
End page 19
Total pages 19
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2021-02-16
ISSN 1424-8220
Keyword(s) Internet of Things
malware
Android OS
feature selection
machine learning
filter methods
malware detection
smartphones
Summary Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.
Language eng
DOI 10.3390/s21041374
Indigenous content off
Field of Research 0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
0502 Environmental Science and Management
0602 Ecology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30148225

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.